Building TorchVision with numpy support

mahmoodn: <p>Hi,<br>
I get the following error from my Python code:</p>
<pre><code class="lang-auto">  File "/disk1/mahmood/vision/torchvision/transforms/functional.py", line 168, in to_tensor
    img = torch.from_numpy(np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True))
RuntimeError: PyTorch was compiled without NumPy support

</code></pre>
<p>That is weird because according to the following command, Pytorch finds Numpy. Look at the last lines:</p>
<pre><code class="lang-auto">$ python3 -m torch.utils.collect_env
/usr/lib/python3.10/runpy.py:126: RuntimeWarning: 'torch.utils.collect_env' found in sys.modules after import of package 'torch.utils', but prior to execution of 'torch.utils.collect_env'; this may result in unpredictable behaviour
  warn(RuntimeWarning(msg))
Collecting environment information...
PyTorch version: 2.5.0a0+gite317a8b
Is debug build: False
CUDA used to build PyTorch: 12.2
ROCM used to build PyTorch: N/A

OS: Ubuntu 22.04.4 LTS (x86_64)
GCC version: (Ubuntu 12.3.0-1ubuntu1~22.04) 12.3.0
Clang version: Could not collect
CMake version: version 3.22.1
Libc version: glibc-2.35

Python version: 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] (64-bit runtime)
Python platform: Linux-5.19.0-41-generic-x86_64-with-glibc2.35
Is CUDA available: True
CUDA runtime version: 12.2.91
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration: GPU 0: NVIDIA GeForce RTX 3080
Nvidia driver version: 535.54.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.2.0
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.2.0
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

CPU:
Architecture:                    x86_64
CPU op-mode(s):                  32-bit, 64-bit
Address sizes:                   43 bits physical, 48 bits virtual
Byte Order:                      Little Endian
CPU(s):                          16
On-line CPU(s) list:             0-15
Vendor ID:                       AuthenticAMD
Model name:                      AMD Ryzen 7 3700X 8-Core Processor
CPU family:                      23
Model:                           113
Thread(s) per core:              2
Core(s) per socket:              8
Socket(s):                       1
Stepping:                        0
Frequency boost:                 enabled
CPU max MHz:                     4426.1709
CPU min MHz:                     2200.0000
BogoMIPS:                        7199.47
Flags:                           fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sev sev_es
Virtualization:                  AMD-V
L1d cache:                       256 KiB (8 instances)
L1i cache:                       256 KiB (8 instances)
L2 cache:                        4 MiB (8 instances)
L3 cache:                        32 MiB (2 instances)
NUMA node(s):                    1
NUMA node0 CPU(s):               0-15
Vulnerability Itlb multihit:     Not affected
Vulnerability L1tf:              Not affected
Vulnerability Mds:               Not affected
Vulnerability Meltdown:          Not affected
Vulnerability Mmio stale data:   Not affected
Vulnerability Retbleed:          Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:        Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:        Mitigation; Retpolines, IBPB conditional, STIBP always-on, RSB filling, PBRSB-eIBRS Not affected
Vulnerability Srbds:             Not affected
Vulnerability Tsx async abort:   Not affected

Versions of relevant libraries:
[pip3] numpy==2.0.0
[pip3] torch==2.5.0a0+gite317a8b
[pip3] torchvision==0.20.0a0+0bf8d14
[conda] Could not collect

</code></pre>
<p>I used the <code>python setup.py develop</code> to build both Pytorch and TorchVision for CUDA-12.2.</p>
<p>So, I don’t know which option to use for TorchVision in order to use Numpy. Any idea?</p>

ptrblck: <p>Could you downgrade <code>numpy</code> to a 1.x release and check if this would fix it?</p>